The automatic data collection (ADC) arts include numerous systems for representing information in machine-readable form. For example, a variety of symbologies exist for representing information in barcode symbols, matrix or area code symbols, and/or stacked symbols. A symbology typically refers to a set of machine-readable symbol characters, some of which are mapped to a set of human-recognizable symbols such as alphabetic characters and/or numeric values. Machine-readable symbols are typically comprised of machine-readable symbol characters selected from the particular symbology to encode information. Machine-readable symbols typically encode information about an object on which the machine-readable symbol is printed, etched, carried or attached to, for example, via packaging or a tag.
Barcode symbols are a common one-dimensional (1D) form of machine-readable symbols. Barcode symbols typically comprise a pattern of vertical bars of various widths separated by spaces of various widths, with information encoded in the relative thickness of the bars and/or spaces, each of which have different light reflecting properties. One-dimensional barcode symbols require a relatively large space to convey a small amount of data.
Two-dimensional symbologies have been developed to increase the data density of machine-readable symbols. Some examples of two-dimensional symbologies include stacked code symbologies. Stacked code symbologies may be employed where length limitations undesirably limit the amount of information in the machine-readable symbol. Stacked code symbols typically employ several lines of vertically stacked one-dimensional symbols. The increase in information density is realized by reducing or eliminating the space that would typically be required between individual barcode symbols.
Some other examples of two-dimensional symbologies include matrix or area code symbologies (hereinafter “matrix code”). A matrix code symbol typically has a two-dimensional perimeter, and comprises a number of geometric elements distributed in a pattern within the perimeter. The perimeter may, for example, be generally square, rectangular or round. The geometric elements may, for example, be square, round, or polygonal, for example hexagonal. The two-dimensional nature of such a machine-readable symbol allows more information to be encoded in a given area than a one-dimensional barcode symbol.
The various above-described machine-readable symbols may or may not also employ color to increase information density.
A variety of machine-readable symbol readers for reading machine-readable symbols are known. Machine-readable symbol readers typically employ one of two fundamental approaches, scanning or imaging.
In scanning, a focused beam of light is scanned across the machine-readable symbol, and light reflected or returned from and modulated by the machine-readable symbol is received by the reader and demodulated. With some readers, the machine-readable symbol is moved past the reader, with other readers the reader is moved past the machine-readable symbol, and still other readers move the beam of light across the machine-readable symbol while the reader and machine-readable symbol remain approximately fixed with respect to one another. Demodulation typically includes an analog-to-digital conversion and a decoding of the resulting digital signal.
Scanning-type machine-readable symbol readers typically employ a source of coherent light such as a laser diode to produce a beam, and employ a beam deflection system such as a rotating or oscillating mirror to scan the resulting beam across the machine-readable symbols. Conventional laser scanning systems employ progressive symbol sampling.
In imaging, the machine-readable symbol reader may flood the machine-readable symbol with light, or may rely on ambient lighting. A one-dimensional (linear) or two-dimensional image (2D) capture device or imager such as a charge coupled device (CCD) array captures a digital image of the illuminated machine-readable symbol, typically by electronically sampling or scanning the pixels of the two-dimensional image capture device. The captured image is then decoded, typically without the need to perform an analog to digital conversion.
A two-dimensional machine-readable symbol reader system may convert, for example, two-dimensional symbols into pixels. See, for example, U.S. Pat. No. 4,988,852 issued to Krishnan, U.S. Pat. No. 5,378,883 issued to Batterman, et al., U.S. Pat. No. 6,330,974 issued to Ackley, U.S. Pat. No. 6,484,944 issued to Manine, et al., and U.S. Pat. No. 6,732,930 issued to Massieu, et al.
Regardless of the type of data carrier used, their usefulness is limited by the capability of a data collection device (such as a matrix code reader, barcode reader, and the like) to accurately capture the data encoded in the machine-readable symbol. Optical data collection devices are directional in nature—such devices need to be optimally positioned in order to accurately read the data on the target symbol. For example, if the data collection device is positioned too far from a target machine-readable symbol, then the target machine-readable symbol may be out of range or otherwise outside of an optimal focus distance of the data collection device. As a result, the data encoded in the target machine-readable system may not be read or may be read incorrectly. The inability of an inexperienced user to skillfully position the data collection device also contributes to the directional limitations of such devices, thereby further contributing to the chances of erroneous or missed data readings.
The quality of the optical resolution of an automatic data collection device is often dependent upon the type of components used by the automatic data collection device in the scanning and/or imaging process. For example, some scanning-type data collection devices use a microelectromechanical structure (MEMS) scanner. The MEMS scanner includes a scanning mirror that oscillates at a resonance frequency to deflect a light beam incident thereon across a target machine-readable symbol.
The size of the scanning mirror can be minimized to allow oscillation at a high frequency, thereby allowing the design of miniature scanners with high throughput capabilities. However, the effects of diffraction limit the maximum resolution that can be obtained when using a scanning mirror having reduced size, particularly when scanning far-field target machine-readable symbols. As a result, it can be difficult for an automatic data collection device having a reduced-size MEMS scanning mirror to effectively read far-field target machine-readable symbols. In short, the small size of such MEMs scanning mirrors inherently limits the optical resolution at far-field.
A possible approach to the above-described problem is to provide the automatic data collection devices with multiple scanning mirrors having different sizes (and thus different resonance frequencies). A particular one of the scanning mirrors can be selected for each specific scanning condition (e.g., reading at a far field, reading at a near field, etc.), thereby providing some basic adaptive capabilities. However, such an approach is not practical due to the costly nature of the MEMS manufacturing process, due to the inflexibility of the automatic data collection device where none of the scanning mirrors is suitable for a particular scanning situation, and/or due to other reasons.